python - 无法连接类型为“的对象”"; 只有 pd.Series、pd.DataFrame 和 pd.Panel(已弃用)obj 有效
问题描述
我的输入数据采用以下形式:
gold,Program,MethodType,CallersT,CallersN,CallersU,CallersCallersT,CallersCallersN,CallersCallersU,CalleesT,CalleesN,CalleesU,CalleesCalleesT,CalleesCalleesN,CalleesCalleesU,CompleteCallersCallees,classGold
T,chess,Inner,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
T,chess,Inner,Low,-1,-1,Low,-1,-1,Medium,-1,Medium,High,-1,High,0,Trace,
T,chess,Inner,Low,-1,-1,Low,-1,-1,Medium,-1,Medium,High,-1,High,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,Low,Low,High,Medium,-1,Medium,0,Trace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,Low,Low,Medium,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,-1,Medium,Medium,0,NoTrace,
T,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,Low,Low,Medium,0,Trace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,-1,Medium,Medium,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,Low,Low,High,Low,Low,Medium,0,Trace,
N,chess,Inner,Low,-1,-1,-1,-1,-1,Low,Low,High,Low,Low,Medium,0,Trace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,-1,Medium,Medium,0,NoTrace,
....
N,chess,Inner,-1,Low,-1,-1,Medium,-1,-1,Low,Low,-1,-1,-1,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Medium,-1,-1,Low,Low,-1,-1,-1,0,NoTrace,
T,chess,Inner,Low,-1,-1,Low,Low,-1,Low,-1,Low,-1,-1,-1,0,Trace,
T,chess,Inner,Low,-1,-1,Medium,-1,-1,Low,-1,Low,-1,-1,-1,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Medium,-1,-1,Low,Low,-1,-1,-1,0,NoTrace,
我正在读取我的数据,并尝试连接两个作为原始数据集子集的数据集,这是我正在使用的代码:
import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
# Feature Scaling
from sklearn.preprocessing import StandardScaler
SeparateProjectLearning=False
CompleteCallersCallees=False
PartialTrainingSetCompleteCallersCallees=True
def main():
X_train={}
X_test={}
y_train={}
y_test={}
dataset = pd.read_csv( 'InputData.txt', sep= ',', index_col=False)
#convert T into 1 and N into 0
dataset['gold'] = dataset['gold'].astype('category').cat.codes
dataset['Program'] = dataset['Program'].astype('category').cat.codes
dataset['classGold'] = dataset['classGold'].astype('category').cat.codes
dataset['MethodType'] = dataset['MethodType'].astype('category').cat.codes
dataset['CallersT'] = dataset['CallersT'].astype('category').cat.codes
dataset['CallersN'] = dataset['CallersN'].astype('category').cat.codes
dataset['CallersU'] = dataset['CallersU'].astype('category').cat.codes
dataset['CallersCallersT'] = dataset['CallersCallersT'].astype('category').cat.codes
dataset['CallersCallersN'] = dataset['CallersCallersN'].astype('category').cat.codes
dataset['CallersCallersU'] = dataset['CallersCallersU'].astype('category').cat.codes
dataset['CalleesT'] = dataset['CalleesT'].astype('category').cat.codes
dataset['CalleesN'] = dataset['CalleesN'].astype('category').cat.codes
dataset['CalleesU'] = dataset['CalleesU'].astype('category').cat.codes
dataset['CalleesCalleesT'] = dataset['CalleesCalleesT'].astype('category').cat.codes
dataset['CalleesCalleesN'] = dataset['CalleesCalleesN'].astype('category').cat.codes
dataset['CalleesCalleesU'] = dataset['CalleesCalleesU'].astype('category').cat.codes
pd.set_option('display.max_columns', None)
row_count, column_count = dataset.shape
Xcol = dataset.iloc[:, 1:column_count]
CompleteSet=dataset.loc[dataset['CompleteCallersCallees'] == 1]
CompleteSet_X = CompleteSet.iloc[:, 1:column_count].values
CompleteSet_Y = CompleteSet.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(CompleteSet_X, CompleteSet_Y, test_size = 0.2, random_state = 0)
TestSet=dataset.loc[dataset['CompleteCallersCallees'] == 0]
X_test1=TestSet.iloc[:, 1:column_count].values
X_test=pd.concat(X_test1,X_test)
X_test1
我想通过使用连接来构建我自己的测试集和训练集,并且我正在尝试X_test
在上面的代码中进行连接。但是,问题是我收到最后一行代码 X_test=pd.concat(X_test1,X_test)
的错误,并且错误显示为TypeError: cannot concatenate object of type "<class 'numpy.ndarray'>"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid
. 我怎样才能解决这个问题?
解决方案
通过在以下几行中添加.values
到过滤器的末尾:
CompleteSet_X = CompleteSet.iloc[:, 1:column_count].values
CompleteSet_Y = CompleteSet.iloc[:, 0].values
X_test1=TestSet.iloc[:, 1:column_count].values
ndarray
您正在从 Pandas Series
/之前的代码提取中提取底层 Numpy DataFrame
,只需在最后删除.values
,您就可以concat
直接与Series
or一起使用DataFrame
。
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